Reliability of complex chemical engineering processes

Usman Abubakar, Srinivas Sriramula, Neill C. Renton

Research output: Contribution to journalArticlepeer-review

22 Citations (Scopus)


This paper presents a stochastic performance modelling approach that can be used to optimise design and operational reliability of complex chemical engineering processes. The framework can be applied to processes comprising multiple units, including the cases where closed form process performance functions are unavailable or difficult to derive from first principles, which is often the case in practice. An interface that facilitates automated two-way communication between Matlab® and process simulation environment is used to generate large process responses. The resulting constrained optimisation problem is solved using both Monte Carlo Simulation (MCS) and First Order Reliability Method (FORM); providing a wide range of stochastic process performance measures. Adding such capabilities to traditional deterministic process simulators provides a more informed basis for selecting optimum design factors; giving a simple way of enhancing overall process reliability and cost-efficiency. Two case study systems are considered to highlight the applicability and benefits of the approach.
Original languageEnglish
Pages (from-to)1–14
Number of pages14
JournalComputers & Chemical Engineering
Early online date25 Dec 2014
Publication statusPublished - 4 Mar 2015

Bibliographical note

The Authors wish to gratefully acknowledge the financial support granted by Petroleum Technology Development Fund (PTDF), Nigeria. Sriramula's work within the Lloyd's Register Foundation Centre for Safety and Reliability Engineering at the University of Aberdeen is supported by Lloyd's Register Foundation. The Foundation helps to protect life and property by supporting engineering-related education, public engagement and the application of research.


  • chemical process reliability
  • design optimisation
  • uncertainty modelling
  • stochastic analysis
  • process performance simulation


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